Jove
Visualize
Contact Us
JoVE
x logofacebook logolinkedin logoyoutube logo
ABOUT JoVE
OverviewLeadershipBlogJoVE Help Center
AUTHORS
Publishing ProcessEditorial BoardScope & PoliciesPeer ReviewFAQSubmit
LIBRARIANS
TestimonialsSubscriptionsAccessResourcesLibrary Advisory BoardFAQ
RESEARCH
JoVE JournalMethods CollectionsJoVE Encyclopedia of ExperimentsArchive
EDUCATION
JoVE CoreJoVE BusinessJoVE Science EducationJoVE Lab ManualFaculty Resource CenterFaculty Site
Terms & Conditions of Use
Privacy Policy
Policies

Related Concept Videos

Force Classification01:22

Force Classification

1.9K
Forces play a crucial role in the study of physics and engineering. They are essential in describing the motion, behavior, and equilibrium of objects in the physical world. Forces can be classified based on their origin, type, and direction of action.
Contact and non-contact forces are two of the most widely used categories of forces. As the name suggests, contact forces require physical contact between two objects to act upon each other. Examples of contact forces include frictional,...
1.9K
Deconvolution01:20

Deconvolution

374
Deconvolution, also known as inverse filtering, is the process of extracting the impulse response from known input and output signals. This technique is vital in scenarios where the system's characteristics are unknown, and they must be inferred from the observable signals.
Deconvolution involves several mathematical techniques to derive the impulse response. One common approach is polynomial division. In this method, the input and output sequences are treated as coefficients of...
374

You might also read

Related Articles

Articles linked to this work by shared authors, journal, and citation graph.

Sort by
Same author

Granulomatous disease, fevers, and progressive cytopenias: What is the underlying diagnosis?

The American journal of medicine·2026
Same author

Targeting noncanonical nuclear factor kappa B signalling in CYLD cutaneous syndrome by selective inhibition of IκB kinase alpha.

The British journal of dermatology·2026
Same author

Is Intracranial Pressure Monitoring After Open Cranial Procedures Associated With Outcome?

The Journal of surgical research·2025
Same author

Seroprevalence of canine distemper virus antibodies in free-roaming dogs in Cambodia.

Veterinary journal (London, England : 1997)·2024
Same author

IL-1β stimulates a novel axis within the NFκB pathway in endothelial cells regulated by IKKα and TAK-1.

Biochemical pharmacology·2024
Same author

Design and Synthesis of Novel Aminoindazole-pyrrolo[2,3-<i>b</i>]pyridine Inhibitors of IKKα That Selectively Perturb Cellular Non-Canonical NF-κB Signalling.

Molecules (Basel, Switzerland)·2024

Related Experiment Video

Updated: Nov 7, 2025

Author Spotlight: Enhancement of Salient Object Detection for Smart Grid Applications
03:31

Author Spotlight: Enhancement of Salient Object Detection for Smart Grid Applications

Published on: December 15, 2023

744

U-Infuse: Democratization of Customizable Deep Learning for Object Detection.

Andrew Shepley1, Greg Falzon1,2, Christopher Lawson1

  • 1School of Science and Technology, University of New England, Armidale, NSW 2350, Australia.

Sensors (Basel, Switzerland)
|April 30, 2021
PubMed
Summary
This summary is machine-generated.

U-Infuse democratizes deep learning for ecologists, enabling custom species detection models without technical expertise. This free software empowers efficient image analysis for biodiversity conservation using camera trap data.

Keywords:
animal identificationartificial intelligencecamera trappingcamera-trap imagesdeep convolutional neural networksdeep learningecological object detectionenvironmental softwarewildlife ecologywildlife monitoring

More Related Videos

A Step-by-Step Implementation of DeepBehavior, Deep Learning Toolbox for Automated Behavior Analysis
05:41

A Step-by-Step Implementation of DeepBehavior, Deep Learning Toolbox for Automated Behavior Analysis

Published on: February 6, 2020

9.6K
Deep Neural Networks for Image-Based Dietary Assessment
13:19

Deep Neural Networks for Image-Based Dietary Assessment

Published on: March 13, 2021

9.6K

Related Experiment Videos

Last Updated: Nov 7, 2025

Author Spotlight: Enhancement of Salient Object Detection for Smart Grid Applications
03:31

Author Spotlight: Enhancement of Salient Object Detection for Smart Grid Applications

Published on: December 15, 2023

744
A Step-by-Step Implementation of DeepBehavior, Deep Learning Toolbox for Automated Behavior Analysis
05:41

A Step-by-Step Implementation of DeepBehavior, Deep Learning Toolbox for Automated Behavior Analysis

Published on: February 6, 2020

9.6K
Deep Neural Networks for Image-Based Dietary Assessment
13:19

Deep Neural Networks for Image-Based Dietary Assessment

Published on: March 13, 2021

9.6K

Area of Science:

  • Ecology
  • Computer Science
  • Biodiversity Conservation

Background:

  • Ecological image analysis, particularly from camera traps, is crucial for biodiversity conservation but is time-consuming and resource-intensive.
  • Current deep learning models often lack generalizability to new environments and require specialized technical skills, creating a barrier for ecologists.
  • There's a need for accessible tools that allow ecologists to develop custom, high-performance models for specific species and environments.

Purpose of the Study:

  • To introduce U-Infuse, a user-friendly, free, and open-source software application designed to democratize deep learning for ecological image analysis.
  • To enable ecologists, regardless of technical background, to train custom object detection models for species identification and distribution analysis.
  • To streamline the process of image annotation and model training, reducing time and resource constraints in ecological research.

Main Methods:

  • U-Infuse provides a graphical user interface (GUI) for training custom deep learning models.
  • The software incorporates auto-annotation and annotation editing features to facilitate dataset creation and quality control.
  • It supports both multiclass and single-class object detection, utilizing publicly available and user-provided image data.

Main Results:

  • U-Infuse allows ecologists to train customized deep learning models on their own devices without requiring advanced computer science expertise or data sharing.
  • The software facilitates efficient image processing, enabling the analysis of larger datasets with reduced time and resource expenditure.
  • Users can generate species distribution reports and other statistical outputs directly from the trained models.

Conclusions:

  • U-Infuse significantly lowers the barrier to entry for applying deep learning in ecological research, particularly for camera trap data analysis.
  • The tool empowers ecologists to develop tailored solutions for species monitoring and conservation, enhancing data-driven decision-making.
  • By protecting intellectual property and privacy, U-Infuse promotes broader adoption and advancement of AI in ecological science.